GIS II: Lab 3, Watershed Analysis

Goals and Background 

The goal of this lab was to use spatial analyst tools to understand watershed analysis and create a map of watersheds in the Adirondack region of New York State. Watershed analysis is a very important concept to understand and be able to use as it is applied in water management, water quality, and conservation to name a few. 

Methods

To begin, data from Cornell University and New York State GIS Clearinghouse was downloaded from the internet. This include a hydrology shapefile an the Adirondak State park polygon. ArcToolbox was heavily utilized in this lab. A 20 km buffer was added to the state park polygon as seen in Figure 1. 

Figure 1. A map showing a 20 km buffer around the state park polygon. The blue lines are the hydrology shapefile from Cornell. These are 2D and have no topographic relation.

An issue with this lab was that not all data was in the same projection. To correct this issue the project tool under data management was used and the projection of the state park shapefile wsa imported. The projection of this shapefile was NAD 1983 Zone 18 N. 

Because a watershed is a function of the topography there needs to be a spatial reference in place. The 30-arc second DEM of North America was imported from ESRI/ArcMap Online. This DEM was also in a different projection and using the transform tool was corrected to be in the same projection. We are only interested in the area of the park and using the clip tool, and new raster dataset was created and the original DEM removed.

The next step is to understand flow direction. Using the flow direction tool under the hydrology tab, nodes are created. Unfortunately, the output contains sinks, or areas that impede water flow. If this map was to be used for watershed analysis the results would be skewed and incorrect so sinks must be filled. The fill tool is used and new nodes are created for flow direction. The result is very trippy and can be seen in Figure 2. 

 

Figure 2. The new nodes are created for flow direction after sinks are filled.  Faint outlines of rivers and streams can be observed. 

Flow accumulation is the understanding of where water accumulates and creates channels. To understand watersheds, flow accumulation must be considered.  Using the flow accumulation tool, a new raster is created.

A source raster also must be created in order to delineate watersheds. This requires a threshold expression. Smaller thresholds yield more watersheds and contain much more detail of in-flowing streams. Larger thresholds yield less watersheds and are more reliant on the larger stream channels. The results figure has a threshold of 50,000.

Figure 3. Watersheds at a higher threshold.

Using the watershed tool,  watersheds for this area are now delineated. At 50k there are 108 different watersheds. The watersheds raster is then clipped to the park boundary buffer. This can be seen in the results section.

Results

Figure 4. The completed map for watershed analysis. The vectorstreams shapefile shows those streams to which contribute for a watershed. These are different from the initial hydrology shapefile as these streams contain a topography factor, are filled, and show accumulation. 

Sources

All materials from this lab are open for public consumption and are property of the University of Wisconsin-Eau Claire, ESRI, Cornerll University, and the New York State GIS Clearinghouse.

GIS II: Lab 2, Georeferencing

Goals and Background:

The goal of this lab is to review the process of georeferencing using ArcGIS software. This will be done using the ESRI georeferencing course and data as well as data from the University of Wisconsin-Eau Claire. 

Methods

For the historical Eau Claire map, the Mcenterlines shapefile is added first and has a projected coordinate system of NAD_1983_HARN_Adj_WI,_EauClaire_feet and a projection of Lambert_conformal_conic. The historical map has no an undefined coordinate system and projection. This means that the Mcenterlines shapefile is the referenced data for the historical crop. 

For both of this data, the georeferencing toolbar is used. To make creating links easier, a side by side screen is used as seen in Figure 1 below. 

Figure 1. Side-by-side view of the historical, undefined Eau Claire map (right) and the georeferenced Mcenterlines map (left). The study area is consistent between the two maps.

Results

Figure 2. Historical map of Eau Claire, WI overlaying the modern Mcenterlines shapefile (in green). The historical map is at 50% transparency. Note the change in water features, ward boundaries, and street names. Historical maps, like this one, are often drawn in an obsolete local grid system and are not very accurate. There was also issues with georeferencing the historical map and getting it as close to the modern basemap and Mcenterlines files as possible.

Figure 3. Comparisons of the water features in the Eau Claire, WI area. The purple outlined area represents the location of water features today and the light blue represents the location of water features in 1878. Some of these changes could be anthropologically caused while some may be naturally caused. Anthropogenic causes include dams, bridges, and flood barriers to name a few.

Sources

All data came from ESRI and the University of Wisconsin-Eau Claire.

GIS II: Lab 1

Goal and Background

The goal of this lab was to reintroduce ArcMap software as taught in GIS 1 and use ArcMap as an analysis tool.

Methods

Using the 'Erie' map from the class data folder the attributes tables was closely examined to understand each record and the relationship between one-another. Using the symbology tab under properties for the Erie map two maps were created for the analysis of tract populations and the number of households per tract.  A map for tract populations was created using the sub-tab 'Categories.'  This data can be normalized but it was not in this instance as this map was going to be used to be compared against the number of households per tract map. Under the sub-tab 'Quantities,' a dot map was created for the second map. 

Results

As expected, there were some similarities between the two maps as these variables are dependent of each other. However, there were some interesting differences such as some tracts have a high population but a few number of households. Further analysis of these maps would be interesting if an median age was recorded and could be analyzed or if a land use feature class was added. These further analyses could reveal or disprove any additional trends.

Sources and References

All data used is from the University of Wisconsin-Eau Claire geography department.